henry

Libraries

github link: https://github.com/becca-belmonte/H99_RNAseq

Load data

Functions

Analysis

First, we can see that the samples primarily differ in expression due to being control or H99 samples. We can also see the top differentially expressed genes, both those higher in control and those higher in H99 mutants.

#rownames(col_data) %in% colnames(raw_counts)

matrix_raw_counts <- as.matrix(raw_counts)

dds <- DESeqDataSetFromMatrix(countData = matrix_raw_counts, 
                                   colData = col_data,
                                   design = ~ Group)

dds <- estimateSizeFactors(dds)

vsdB = varianceStabilizingTransformation(dds)
plotPCA(vsdB,intgroup = c("Group"))

ddsTC <- DESeq(dds)
resTC <- results(ddsTC)

resTC$gene <- rownames(resTC)
tab_resTC <- as.data.frame(resTC)
tab_resTC$gene <- rownames(tab_resTC)

tab_resTC <- cbind(tab_resTC, Gene_ID_Flybase[match(tab_resTC$gene, Gene_ID_Flybase$FBgn_ID), "Gene_name"])
colnames(tab_resTC)[ncol(tab_resTC)] = "Gene_name"

tab_resTC <- cbind(tab_resTC, Gene_ID_Flybase[match(tab_resTC$gene, Gene_ID_Flybase$FBgn_ID), "CG_ID"])
colnames(tab_resTC)[ncol(tab_resTC)] = "CG_ID"

tab_resTC <- tab_resTC %>% 
  mutate(Gene_name = coalesce(Gene_name, gene))

tab_top <- tab_resTC %>% 
  filter(abs(log2FoldChange) > 1 & padj < 0.05) %>% 
  arrange(padj)

tab_top_down <- tab_top %>% 
  filter(log2FoldChange < 0)

kable(head(tab_top, 10)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>% 
  scroll_box(width = "100%")

	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	gene	Gene_name	CG_ID
FBgn0085466	43263.1941	4.824477	0.2282038	21.14109	0	0	FBgn0085466	FBgn0085466	NA
FBgn0020493	4746.8218	-5.549997	0.2652973	-20.91992	0	0	FBgn0020493	Dad	CG5201
FBgn0037930	6747.1360	3.919628	0.2287028	17.13852	0	0	FBgn0037930	CG14715	CG14715
FBgn0052085	1674.8032	8.845837	0.5171809	17.10395	0	0	FBgn0052085	FBgn0052085	NA
FBgn0023129	25753.8140	2.400866	0.1460641	16.43707	0	0	FBgn0023129	aay	CG3705
FBgn0025678	53362.0963	-3.282450	0.2067646	-15.87530	0	0	FBgn0025678	CaBP1	CG5809
FBgn0037222	22074.0559	-2.704999	0.1858704	-14.55314	0	0	FBgn0037222	CG14642	CG14642
FBgn0039099	15792.9504	-2.203793	0.1717471	-12.83161	0	0	FBgn0039099	GILT2	CG10157
FBgn0051813	3122.6405	3.710390	0.2991312	12.40389	0	0	FBgn0051813	nur	CG31813
FBgn0267472	396.0726	-5.570781	0.4826268	-11.54263	0	0	FBgn0267472	asRNA:CR45822	CR45822

#write.csv(tab_top, file = "Results/tab_top.csv")

GSEA

To get a better idea of what these genes are doing, I did a gene set enrichment analysis. First I looked at the reactome and unfortunately, none of them are significant with the adjusted p-value. But, we do see that Golgi Associated Vesicle Biogenesis is particularly enriched in H99 mutants when compared to the control. I then looked at KEGG pathways and saw that the phagosome pathway was enriched in control samples when compared to H99 mutants.

colors_vol <- c(rev(viridis(2)), "darkgrey")

ranks <- tab_resTC$log2FoldChange[!is.na(tab_resTC$log2FoldChange)]
names(ranks) <- tab_resTC$CG_ID[!is.na(tab_resTC$log2FoldChange)]
#head(ranks)
#barplot(sort(ranks, decreasing = T))

fgseaRes <- fgsea(t_Drosophila_REACTOME, ranks, minSize = 15, maxSize = 500)
head(fgseaRes[order(padj, -abs(NES)), ], n=10)

##                                                                 pathway
##  1:                                 Golgi Associated Vesicle Biogenesis
##  2:                                      Phosphorylation of PER and TIM
##  3:                          Signaling by Non-Receptor Tyrosine Kinases
##  4:                                                   Signaling by PTK6
##  5:                                                 Intra-Golgi traffic
##  6:                                   RNA Polymerase II Promoter Escape
##  7:                          RNA Polymerase II Transcription Initiation
##  8:   RNA Polymerase II Transcription Initiation And Promoter Clearance
##  9: RNA Polymerase II Transcription Pre-Initiation And Promoter Opening
## 10:                                           MAP2K and MAPK activation
##            pval padj   log2err         ES       NES size
##  1: 0.002945886    1 0.4317077  0.7107300  1.724223   27
##  2: 0.020256094    1 0.3524879  0.7349901  1.554007   15
##  3: 0.028444229    1 0.3524879  0.6875806  1.527877   18
##  4: 0.028444229    1 0.3524879  0.6875806  1.527877   18
##  5: 0.022954825    1 0.3524879  0.6294295  1.526989   27
##  6: 0.021969374    1 0.3524879 -0.6054070 -1.508175   38
##  7: 0.021969374    1 0.3524879 -0.6054070 -1.508175   38
##  8: 0.021969374    1 0.3524879 -0.6054070 -1.508175   38
##  9: 0.021969374    1 0.3524879 -0.6054070 -1.508175   38
## 10: 0.070981211    1 0.2450418  0.6082707  1.448003   24
##                                          leadingEdge
##  1:                            CG32683,CG4349,CG5711
##  2:                                    CG3772,CG2048
##  3:                     CG3927,CG10384,CG3875,CG4021
##  4:                     CG3927,CG10384,CG3875,CG4021
##  5:       CG10592,CG5150,CG3292,CG7980,CG8105,CG8147
##  6:                     CG14718,CG7562,CG9879,CG3180
##  7:                     CG14718,CG7562,CG9879,CG3180
##  8:                     CG14718,CG7562,CG9879,CG3180
##  9:                     CG14718,CG7562,CG9879,CG3180
## 10: CG32683,CG5711,CG31832,CG7524,CG8642,CG10359,...

plotEnrichment(t_Drosophila_REACTOME[["Golgi Associated Vesicle Biogenesis"]], ranks) +
  ggtitle("Golgi Associated Vesicle Biogenesis")

fgseaRes <- fgseaRes %>% 
  mutate(color = factor(case_when(NES >= 1 & pval < 0.05 ~ "blue", 
                                  NES <= -1 & pval < 0.05 ~ "red", 
                                  NES %in% c(-1:1) ~ "grey")))

ggplot(fgseaRes, aes(x = NES, y = -log10(pval))) +
  geom_point(aes(color = color))+
  geom_text_repel(data = (fgseaRes %>% filter(pval < 0.025)),aes(label = pathway)) +
  ggtitle("Enriched pathways without regulators of apoptosis") +
  theme_light()+
  scale_colour_manual(limits=c("blue","red", "black"),
                      values = colors_vol,
                      labels=c("Enriched in H99","Enriched in control","ns")) +
  labs(color = "")

fgseaRes_KEGG <- fgsea(t_Drosophila_KEGG, ranks, minSize = 15, maxSize = 500)
head(fgseaRes_KEGG[order(padj, -abs(NES)), ], n=10)

##                                     pathway       pval padj   log2err
##  1:                               Phagosome 0.04447955    1 0.3217759
##  2: Biosynthesis of unsaturated fatty acids 0.10052910    1 0.1864326
##  3:                    Galactose metabolism 0.14010508    1 0.1552420
##  4:             Basal transcription factors 0.11981567    1 0.1957890
##  5:             Insect hormone biosynthesis 0.14712644    1 0.1752040
##  6:              Apoptosis_multiple species 0.17040359    1 0.1596467
##  7:                        ABC transporters 0.19964974    1 0.1275053
##  8:      Cysteine and methionine metabolism 0.19237435    1 0.1294429
##  9:                   N Glycan biosynthesis 0.17667845    1 0.1372508
## 10:            Longevity regulating pathway 0.16067146    1 0.1709323
##             ES       NES size
##  1: -0.5395880 -1.446518   57
##  2: -0.6490166 -1.365043   16
##  3: -0.6023040 -1.314794   19
##  4:  0.5035614  1.293475   32
##  5:  0.5763314  1.264763   16
##  6:  0.5861498  1.264691   15
##  7: -0.5730456 -1.250924   19
##  8: -0.5299475 -1.243260   27
##  9: -0.5038413 -1.228812   34
## 10:  0.4696273  1.223888   38
##                                            leadingEdge
##  1:      CG8310,CG1076,CG9906,CG1924,CG7794,CG7678,...
##  2:                     CG15531,CG9747,CG18609,CG10849
##  3:  CG8695,CG14934,CG12766,CG32444,CG10638,CG5165,...
##  4:                      CG15632,CG11639,CG5444,CG1276
##  5: CG13478,CG40486,CG31075,CG40485,CG10594,CG6578,...
##  6:         CG8238,CG6531,CG8091,CG7788,CG6829,CG14902
##  7:               CG4562,CG8799,CG8908,CG10181,CG31792
##  8:                             CG11899,CG6287,CG31115
##  9:                                     CG4871,CG17173
## 10:    CG14173,CG14167,CG7756,CG7978,CG5948,CG1506,...

plotEnrichment(t_Drosophila_KEGG[["Phagosome"]], ranks) +
  ggtitle("Phagosome")

fgseaRes_KEGG <- fgseaRes_KEGG %>% 
  mutate(color = factor(case_when(NES >= 1 & pval < 0.05 ~ "blue", 
                                  NES <= -1 & pval < 0.05 ~ "red", 
                                  NES %in% c(-1:1) ~ "grey")))

ggplot(fgseaRes_KEGG, aes(x = NES, y = -log10(pval))) +
  geom_point(aes(color = color))+
  geom_text_repel(data = (fgseaRes_KEGG %>% filter(pval < 0.1)),aes(label = pathway)) +
  ggtitle("Enriched KEGG pathways without regulators of apoptosis") +
  theme_light()+
  scale_colour_manual(limits=c("blue","red", "black"),
                      values = colors_vol,
                      labels=c("Enriched in H99","Enriched in control","ns")) +
  labs(color = "")

phago_genes <- tab_resTC %>% 
  filter(CG_ID %in% t_Drosophila_KEGG[["Phagosome"]])

top_phago_genes <- c("FBgn0264077", "FBgn0003884", "FBgn0005671", "FBgn0040377", "FBgn0261797")

Raw counts

I wanted to get a better idea of which genes were lost in H99 mutants, and particularly those in the phagosome pathway.

best12 <- head(tab_top_down,12)
best12_genes <- best12$gene

col_data <- col_data %>% 
  arrange(ID)

normalized <- normalized_counts %>% 
  mutate(gene = rownames(normalized_counts)) %>% 
  filter(gene %in% best12_genes) %>% 
  select(-gene)
normalized <- as.data.frame(t(normalized))
normalized <- normalized %>% 
  mutate(group = rownames(normalized)) %>% 
  arrange(group) %>% 
  cbind(col_data) %>% 
  pivot_longer(best12_genes, names_to = "gene", values_to = "counts")
normalized$Rep <- as.factor(normalized$Rep)

normalized <- cbind(normalized, Gene_ID_Flybase[match(normalized$gene, Gene_ID_Flybase$FBgn_ID), "Gene_name"])
colnames(normalized)[ncol(normalized)] = "Gene_name"
normalized <- normalized %>% 
  mutate(Gene_name = coalesce(Gene_name, gene))

(rawcounts <- ggplot(normalized, aes(x = Group, y = counts)) +
  geom_point(aes(color = Group), position = position_dodge(width = 0.75)) +
  scale_color_manual(values = (viridis(2))) +
  facet_wrap(~Gene_name, scale = "free_y") +
  theme_classic() +
  xlab("Genotype") +
  ylab("Normalized counts") +
  ggtitle("Top differentially expressed genes without apoptosis"))

col_data <- col_data %>% 
  arrange(ID)

normalized <- normalized_counts %>% 
  mutate(gene = rownames(normalized_counts)) %>% 
  filter(gene %in% top_phago_genes) %>% 
  select(-gene)
normalized <- as.data.frame(t(normalized))
normalized <- normalized %>% 
  mutate(group = rownames(normalized)) %>% 
  arrange(group) %>% 
  cbind(col_data) %>% 
  pivot_longer(top_phago_genes, names_to = "gene", values_to = "counts")
normalized$Rep <- as.factor(normalized$Rep)

normalized <- cbind(normalized, Gene_ID_Flybase[match(normalized$gene, Gene_ID_Flybase$FBgn_ID), "Gene_name"])
colnames(normalized)[ncol(normalized)] = "Gene_name"
normalized <- normalized %>% 
  mutate(Gene_name = coalesce(Gene_name, gene))

(rawcounts <- ggplot(normalized, aes(x = Group, y = counts)) +
  geom_point(aes(color = Group), position = position_dodge(width = 0.75)) +
  scale_color_manual(values = (viridis(2))) +
  facet_wrap(~Gene_name, scale = "free_y") +
  theme_classic() +
  xlab("Genotype") +
  ylab("Normalized counts") +
  ggtitle("Top differentially expressed phagosome-associated genes without apoptosis"))

Heatmap

Volcano plot

With the volcano plot, we can see that Dad and CaBP1 are downregulated in H99 mutants, while FBgn0085466 (which is CG34437) and CG14715 are upregulated in H99 mutants.

colors_vol <- c(rev(viridis(2)), "darkgrey")
tab_resTC <- tab_resTC %>% 
  mutate(color = factor(case_when(log2FoldChange >= 2 & padj < 0.05 ~ "up", 
                                  log2FoldChange <= -2 & padj < 0.05 ~ "down", 
                                  log2FoldChange %in% c(-2:2) ~ "ns"))) %>% 
  mutate(significance = case_when(log2FoldChange >= 2 ~ "yes", log2FoldChange <= -2 ~ "yes"))
significant <- subset(tab_resTC, significance=="yes")
sig_genes <- significant$gene

tab_resTC$color <- as.character(tab_resTC$color)
tab_resTC$color <- replace_na(tab_resTC$color,"ns")

tab_resTC <- tab_resTC %>% 
  mutate(Gene_name = coalesce(Gene_name, gene))


(carcass <- ggplot(tab_resTC, aes(x = log2FoldChange, y = -log10(pvalue), label = Gene_name)) +
  geom_point(aes(color = color), alpha = 0.5)+
  geom_text_repel(data = tab_resTC %>% filter(log2FoldChange < -2 | log2FoldChange > 2.5) %>% filter(pvalue <10^-5), aes(label = Gene_name)) +
  geom_vline(xintercept=2,color="darkgrey", linetype = "dotted")+
  geom_vline(xintercept=-2,color="darkgrey", linetype = "dotted")+
  geom_hline(yintercept=-log10(0.05),color="darkgrey",linetype="dashed") +
  theme_bw() +
  scale_colour_manual(limits=c("up","down", "ns"),
                      values = colors_vol,
                      labels=c("Upregulated in H99","Upregulated in control","ns")) +
  labs(color = "") +
  ggtitle("Differential expression in control and H99 mutants"))

ggplotly(carcass)

GO

The first table shows GO terms that are enriched in upregulated genes, so those that are higher in H99 mutants than control. The second table shows GO terms enriched in downregulated genes, those that are lower in H99 mutants.

mart <- useDataset(dataset = "dmelanogaster_gene_ensembl",
                   mart = useMart("ENSEMBL_MART_ENSEMBL"))
resultTable <- getBM(attributes = c("flybase_gene_id", "external_gene_name","go_id", "name_1006", "definition_1006"),
                     mart = mart)
resultTable <- resultTable[resultTable$go_id != '',]
geneID2GO <- by(resultTable$go_id,
                resultTable$external_gene_name,
                function(x) as.character(x))

datRef <- tab_resTC$Gene_name #full set of genes in your analysis (ie. rownames of your summarised experiment input into DEseq2)
tab_top_up <- tab_top %>% 
  filter(log2FoldChange > 0)

theGenes <- tab_top_up$Gene_name#set of flybase IDs that you want to query

geneNames <- tab_resTC$Gene_name
myInterestingGenes <- tab_top_up$Gene_name

geneList <- tab_resTC %>% 
  filter(Gene_name %in% myInterestingGenes) %>% 
  na.omit(geneList)
geneList <- geneList %>% 
  arrange(pvalue)
geneList_pvalue <- geneList[,5]
geneList_name <- as.character(geneList$Gene_name)
names(geneList_pvalue) <- geneList_name
geneList <- geneList_pvalue

#write.csv(geneList_F_UC_8,"geneList_F_UC_8.csv",col.names=F,row.names=T)


all_genes <- sort(unique(as.character(resultTable$external_gene_name)))
int_genes <- factor(as.integer(all_genes %in% geneList_name))
names(int_genes) = all_genes

GOdata <- new("topGOdata", ontology = "BP", allGenes = int_genes, 
              annot = annFUN.gene2GO, gene2GO = geneID2GO)

GO_results_classic <- runTest(GOdata, algorithm = "classic", statistic = "Fisher")
GO_results_elim <- runTest(GOdata, algorithm = "elim", statistic = "Fisher")
GO_results_tab_up <- GenTable(object = GOdata, classic = GO_results_classic, elim = GO_results_elim, orderBy = "elim", ranksOf = "classic", topNodes = 50)

kable(GO_results_tab_up) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>% scroll_box(width = "100%")

GO.ID	Term	Annotated	Significant	Expected	Rank in classic	classic	elim
GO:0098754	detoxification	87	4	0.55	1	0.0023	0.0023
GO:0043153	entrainment of circadian clock by photop…	12	2	0.08	2	0.0025	0.0025
GO:0009785	blue light signaling pathway	1	1	0.01	4	0.0064	0.0064
GO:0010835	regulation of protein ADP-ribosylation	1	1	0.01	5	0.0064	0.0064
GO:0014853	regulation of excitatory postsynaptic me…	1	1	0.01	6	0.0064	0.0064
GO:0018312	peptidyl-serine ADP-ribosylation	1	1	0.01	7	0.0064	0.0064
GO:0030216	keratinocyte differentiation	1	1	0.01	8	0.0064	0.0064
GO:0032481	positive regulation of type I interferon…	1	1	0.01	9	0.0064	0.0064
GO:0050843	S-adenosylmethionine catabolic process	1	1	0.01	10	0.0064	0.0064
GO:0050980	detection of light stimulus involved in …	1	1	0.01	11	0.0064	0.0064
GO:0071465	cellular response to desiccation	1	1	0.01	12	0.0064	0.0064
GO:1901054	sarcosine biosynthetic process	1	1	0.01	13	0.0064	0.0064
GO:0030001	metal ion transport	285	6	1.82	25	0.0096	0.0096
GO:0007610	behavior	588	9	3.75	26	0.0122	0.0122
GO:0006111	regulation of gluconeogenesis	2	1	0.01	27	0.0127	0.0127
GO:0009588	UV-A, blue light phototransduction	2	1	0.01	28	0.0127	0.0127
GO:0009639	response to red or far red light	2	1	0.01	29	0.0127	0.0127
GO:0010114	response to red light	2	1	0.01	30	0.0127	0.0127
GO:0015770	sucrose transport	2	1	0.01	31	0.0127	0.0127
GO:0048150	behavioral response to ether	2	1	0.01	32	0.0127	0.0127
GO:0061965	positive regulation of entry into reprod…	2	1	0.01	33	0.0127	0.0127
GO:0071000	response to magnetism	2	1	0.01	34	0.0127	0.0127
GO:0071929	alpha-tubulin acetylation	2	1	0.01	35	0.0127	0.0127
GO:0120176	positive regulation of torso signaling p…	2	1	0.01	36	0.0127	0.0127
GO:0001752	compound eye photoreceptor fate commitme…	77	3	0.49	39	0.0130	0.0130
GO:0042706	eye photoreceptor cell fate commitment	77	3	0.49	40	0.0130	0.0130
GO:0007218	neuropeptide signaling pathway	78	3	0.50	41	0.0135	0.0135
GO:0048878	chemical homeostasis	314	6	2.00	42	0.0149	0.0149
GO:0042592	homeostatic process	510	8	3.25	43	0.0156	0.0156
GO:0046552	photoreceptor cell fate commitment	84	3	0.54	45	0.0164	0.0164
GO:0098659	inorganic cation import across plasma me…	32	2	0.20	46	0.0176	0.0176
GO:0099587	inorganic ion import across plasma membr…	32	2	0.20	47	0.0176	0.0176
GO:0030534	adult behavior	157	4	1.00	48	0.0179	0.0179
GO:0006564	L-serine biosynthetic process	3	1	0.02	50	0.0190	0.0190
GO:0008302	female germline ring canal formation, ac…	3	1	0.02	51	0.0190	0.0190
GO:0030046	parallel actin filament bundle assembly	3	1	0.02	52	0.0190	0.0190
GO:0045472	response to ether	3	1	0.02	53	0.0190	0.0190
GO:0046498	S-adenosylhomocysteine metabolic process	3	1	0.02	54	0.0190	0.0190
GO:0007155	cell adhesion	242	5	1.54	57	0.0190	0.0190
GO:0008543	fibroblast growth factor receptor signal…	37	2	0.24	59	0.0232	0.0232
GO:0043270	positive regulation of ion transport	37	2	0.24	60	0.0232	0.0232
GO:0044344	cellular response to fibroblast growth f…	37	2	0.24	61	0.0232	0.0232
GO:0071774	response to fibroblast growth factor	37	2	0.24	62	0.0232	0.0232
GO:0008340	determination of adult lifespan	172	4	1.10	63	0.0241	0.0241
GO:0000768	syncytium formation by plasma membrane f…	38	2	0.24	64	0.0243	0.0243
GO:0006949	syncytium formation	38	2	0.24	65	0.0243	0.0243
GO:0007520	myoblast fusion	38	2	0.24	66	0.0243	0.0243
GO:0014902	myotube differentiation	38	2	0.24	67	0.0243	0.0243
GO:0140253	cell-cell fusion	38	2	0.24	68	0.0243	0.0243
GO:0048663	neuron fate commitment	99	3	0.63	70	0.0253	0.0253

tab_top_down <- tab_top %>% 
  filter(log2FoldChange < 0)

theGenes <- tab_top_down$Gene_name#set of flybase IDs that you want to query

geneNames <- tab_resTC$Gene_name
myInterestingGenes <- tab_top_down$Gene_name

geneList <- tab_resTC %>% 
  filter(Gene_name %in% myInterestingGenes) %>% 
  na.omit(geneList)
geneList <- geneList %>% 
  arrange(pvalue)
geneList_pvalue <- geneList[,5]
geneList_name <- as.character(geneList$Gene_name)
names(geneList_pvalue) <- geneList_name
geneList <- geneList_pvalue

#write.csv(geneList_F_UC_8,"geneList_F_UC_8.csv",col.names=F,row.names=T)


all_genes <- sort(unique(as.character(resultTable$external_gene_name)))
int_genes <- factor(as.integer(all_genes %in% geneList_name))
names(int_genes) = all_genes

GOdata <- new("topGOdata", ontology = "BP", allGenes = int_genes, 
              annot = annFUN.gene2GO, gene2GO = geneID2GO)

GO_results_classic <- runTest(GOdata, algorithm = "classic", statistic = "Fisher")
GO_results_elim <- runTest(GOdata, algorithm = "elim", statistic = "Fisher")
GO_results_tab_down <- GenTable(object = GOdata, classic = GO_results_classic, elim = GO_results_elim, orderBy = "elim", ranksOf = "classic", topNodes = 50)

kable(GO_results_tab_down) %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>% scroll_box(width = "100%")

GO.ID	Term	Annotated	Significant	Expected	Rank in classic	classic	elim
GO:0007094	mitotic spindle assembly checkpoint sign…	15	3	0.10	1	0.00013	0.00013
GO:0007443	Malpighian tubule morphogenesis	44	4	0.30	13	0.00022	0.00022
GO:0035002	liquid clearance, open tracheal system	24	3	0.16	19	0.00055	0.00055
GO:0048133	male germ-line stem cell asymmetric divi…	8	2	0.05	27	0.00125	0.00125
GO:0008362	chitin-based embryonic cuticle biosynthe…	17	2	0.12	44	0.00582	0.00582
GO:0010991	negative regulation of SMAD protein comp…	1	1	0.01	46	0.00680	0.00680
GO:0032499	detection of peptidoglycan	1	1	0.01	47	0.00680	0.00680
GO:0033316	meiotic spindle assembly checkpoint sign…	1	1	0.01	48	0.00680	0.00680
GO:0042940	D-amino acid transport	1	1	0.01	49	0.00680	0.00680
GO:0043060	meiotic metaphase I plate congression	1	1	0.01	50	0.00680	0.00680
GO:0060394	negative regulation of pathway-restricte…	1	1	0.01	51	0.00680	0.00680
GO:0061734	parkin-mediated stimulation of mitophagy…	1	1	0.01	52	0.00680	0.00680
GO:1901526	positive regulation of mitophagy	1	1	0.01	53	0.00680	0.00680
GO:1901693	negative regulation of compound eye reti…	1	1	0.01	54	0.00680	0.00680
GO:1904068	G protein-coupled receptor signaling pat…	1	1	0.01	55	0.00680	0.00680
GO:1905342	positive regulation of protein localizat…	1	1	0.01	56	0.00680	0.00680
GO:0006030	chitin metabolic process	21	2	0.14	68	0.00883	0.00883
GO:0035214	eye-antennal disc development	64	3	0.44	69	0.00938	0.00938
GO:0060438	trachea development	23	2	0.16	71	0.01054	0.01054
GO:0051240	positive regulation of multicellular org…	197	5	1.34	73	0.01094	0.01094
GO:0031644	regulation of nervous system process	24	2	0.16	74	0.01145	0.01145
GO:0044057	regulation of system process	69	3	0.47	76	0.01152	0.01152
GO:0042753	positive regulation of circadian rhythm	25	2	0.17	77	0.01239	0.01239
GO:0061983	meiosis II cell cycle process	26	2	0.18	79	0.01337	0.01337
GO:0006037	cell wall chitin metabolic process	2	1	0.01	81	0.01356	0.01356
GO:0006038	cell wall chitin biosynthetic process	2	1	0.01	82	0.01356	0.01356
GO:0007066	female meiosis sister chromatid cohesion	2	1	0.01	83	0.01356	0.01356
GO:0009305	protein biotinylation	2	1	0.01	84	0.01356	0.01356
GO:0010460	positive regulation of heart rate	2	1	0.01	85	0.01356	0.01356
GO:0018054	peptidyl-lysine biotinylation	2	1	0.01	86	0.01356	0.01356
GO:0045760	positive regulation of action potential	2	1	0.01	87	0.01356	0.01356
GO:0048075	positive regulation of eye pigmentation	2	1	0.01	88	0.01356	0.01356
GO:0048078	positive regulation of compound eye pigm…	2	1	0.01	89	0.01356	0.01356
GO:0051971	positive regulation of transmission of n…	2	1	0.01	90	0.01356	0.01356
GO:0060279	positive regulation of ovulation	2	1	0.01	91	0.01356	0.01356
GO:0071110	histone biotinylation	2	1	0.01	92	0.01356	0.01356
GO:0110123	regulation of myotube cell migration	2	1	0.01	93	0.01356	0.01356
GO:0110125	negative regulation of myotube cell migr…	2	1	0.01	94	0.01356	0.01356
GO:1904061	positive regulation of locomotor rhythm	2	1	0.01	95	0.01356	0.01356
GO:1904457	positive regulation of neuronal action p…	2	1	0.01	96	0.01356	0.01356
GO:1905050	positive regulation of metallopeptidase …	2	1	0.01	97	0.01356	0.01356
GO:0007448	anterior/posterior pattern specification…	27	2	0.18	112	0.01437	0.01437
GO:0015807	L-amino acid transport	28	2	0.19	113	0.01541	0.01541
GO:1902475	L-alpha-amino acid transmembrane transpo…	28	2	0.19	114	0.01541	0.01541
GO:0043171	peptide catabolic process	29	2	0.20	115	0.01648	0.01648
GO:0000705	achiasmate meiosis I	3	1	0.02	118	0.02027	0.02027
GO:0008614	pyridoxine metabolic process	3	1	0.02	119	0.02027	0.02027
GO:0008615	pyridoxine biosynthetic process	3	1	0.02	120	0.02027	0.02027
GO:0043567	regulation of insulin-like growth factor…	3	1	0.02	121	0.02027	0.02027
GO:0045678	positive regulation of R7 cell different…	3	1	0.02	122	0.02027	0.02027